A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by Decision-Level Integration of Multi-Omics Data

Mitchel, Joseph F.; Chatlin, Kevin; Li, Tong; Wang, May D.

doi:10.1109/bibm47256.2019.8983243

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…Among the four -omics data, miRNA expression is the most predictive for overall survival, followed by DNA methylation and gene expression. Moreover, CNVs are the least predictive for breast cancer overall survival, which is consistent with our previous findings [20]. The best single-omics survival analysis performance is a C-index of 0.616 ± 0.057, achieved by miRNA data with PCA features.…”

Section: Multi-modality Integration For Breast Cancer Survival Analysissupporting

confidence: 91%

“…In our previous study [20], we have built a transnational pipeline for overall survival prediction of breast cancer patients by decision-level integration of multiomics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)). However, many right-censored samples have been discarded to enable binary classification.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis

Mitchel

Chatlin

et al. 2020

BMC Med Inform Decis Mak

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Background Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival lengths, indicating a need to identify prognostic biomarkers for personalized diagnosis and treatment. With the development of new technologies such as next-generation sequencing, multi-omics information are becoming available for a more thorough evaluation of a patient’s condition. In this study, we aim to improve breast cancer overall survival prediction by integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)). Methods Motivated by multi-view learning, we propose a novel strategy to integrate multi-omics data for breast cancer survival prediction by applying complementary and consensus principles. The complementary principle assumes each -omics data contains modality-unique information. To preserve such information, we develop a concatenation autoencoder (ConcatAE) that concatenates the hidden features learned from each modality for integration. The consensus principle assumes that the disagreements among modalities upper bound the model errors. To get rid of the noises or discrepancies among modalities, we develop a cross-modality autoencoder (CrossAE) to maximize the agreement among modalities to achieve a modality-invariant representation. We first validate the effectiveness of our proposed models on the MNIST simulated data. We then apply these models to the TCCA breast cancer multi-omics data for overall survival prediction. Results For breast cancer overall survival prediction, the integration of DNA methylation and miRNA expression achieves the best overall performance of 0.641 ± 0.031 with ConcatAE, and 0.63 ± 0.081 with CrossAE. Both strategies outperform baseline single-modality models using only DNA methylation (0.583 ± 0.058) or miRNA expression (0.616 ± 0.057). Conclusions In conclusion, we achieve improved overall survival prediction performance by utilizing either the complementary or consensus information among multi-omics data. The proposed ConcatAE and CrossAE models can inspire future deep representation-based multi-omics integration techniques. We believe these novel multi-omics integration models can benefit the personalized diagnosis and treatment of breast cancer patients.

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Section: Multi-modality Integration For Breast Cancer Survival Analysissupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis

Mitchel

Chatlin

et al. 2020

BMC Med Inform Decis Mak

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“…The present study used a multi-layer perceptron neural network (MLPNN). Despite the increased complexity of interpreting neural network outputs compared to outputs of other statistical models, the ANN model has been widely used in various medical fields [ 13 , 14 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study

Lou

Hou

Chang

et al. 2020

Cancers

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No studies have discussed machine learning algorithms to predict recurrence within 10 years after breast cancer surgery. This study purposed to compare the accuracy of forecasting models to predict recurrence within 10 years after breast cancer surgery and to identify significant predictors of recurrence. Registry data for breast cancer surgery patients were allocated to a training dataset (n = 798) for model development, a testing dataset (n = 171) for internal validation, and a validating dataset (n = 171) for external validation. Global sensitivity analysis was then performed to evaluate the significance of the selected predictors. Demographic characteristics, clinical characteristics, quality of care, and preoperative quality of life were significantly associated with recurrence within 10 years after breast cancer surgery (p < 0.05). Artificial neural networks had the highest prediction performance indices. Additionally, the surgeon volume was the best predictor of recurrence within 10 years after breast cancer surgery, followed by hospital volume and tumor stage. Accurate recurrence within 10 years prediction by machine learning algorithms may improve precision in managing patients after breast cancer surgery and improve understanding of risk factors for recurrence within 10 years after breast cancer surgery.

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“…While in this work we focused on tumor transcriptome data which can be measured with high precision over a wide dynamic range of transcript abundances by RNA-seq, we note that TCGA datasets of tumor somatic mutations and copy number alteration events are also available (Hutter and Zenklusen, 2018). Given the voluminous literature on the use of tumor somatic genomic data for precision cancer diagnosis (Mitchel et al , 2019; Zhang et al , 2020; Lee et al , 2019), tumor DNA datasets are fertile ground for developing a semi-supervised, multi-omics model for predicting response to chemotherapy.…”

Section: Discussionmentioning

confidence: 99%

Predicting chemotherapy response using a variational autoencoder approach

Wei

Ramsey

2021

Preprint

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MotivationMultiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are publicly available for thousands of tumors for many cancer types, a relatively modest number of tumor profiles are clinically annotated for response to chemotherapy. The paucity of labeled examples and high dimension of the feature data limit performance for predicting therapeutic response using fully-supervised classification methods. Recently, multiple studies have established the utility of a deep neural network approach, the variational autoencoder (VAE), for generating meaningful latent features from original data. Here, we report first study of a semi-supervised approach using VAE-encoded tumor transcriptome features and regularized gradient boosted decision trees (XGBoost) to predict chemotherapy drug response for five cancer types: colon adenocarcinoma, pancreatic adenocarcinoma, bladder carcinoma, sarcoma, and breast invasive carcinoma.ResultsWe found: (1) VAE-encoding of the tumor transcriptome preserves the cancer type identity of the tumor, suggesting preservation of biologically relevant information; and (2) as a feature-set for supervised classification to predict response-to-chemotherapy, the unsupervised VAE encoding of the tumor’s gene expression profile leads to better area under the receiver operating characteristic curve (AUROC) classification performance than either the original gene expression profile or the PCA principal components of the gene expression profile, in four out of five cancer types that we tested.Availabilitygithub.com/ATHED/VAE_for_chemotherapy_drug_response_predictionContactramseyst@oregonstate.eduSupplementary informationSupplementary data are available at Bioinformatics online.

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A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by Decision-Level Integration of Multi-Omics Data

Cited by 10 publications

References 15 publications

Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis

Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis

Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study

Predicting chemotherapy response using a variational autoencoder approach

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